U.S. patent number 8,788,338 [Application Number 13/932,766] was granted by the patent office on 2014-07-22 for unified marketplace for advertisements and content in an online system.
This patent grant is currently assigned to Yahoo! Inc.. The grantee listed for this patent is Yahoo! Inc.. Invention is credited to Scott J. Gaffney, Prabhakar Krishnamurthy, Jean-Marc Langlois, Aharon Lavi, Bruno Fernandez Ruiz.
United States Patent |
8,788,338 |
Ruiz , et al. |
July 22, 2014 |
Unified marketplace for advertisements and content in an online
system
Abstract
A server system of an online information system displays
advertising items and content items retrieved from storage devices
as a stream viewable by a user on a user device. The advertisement
items and the content items are ordered in the stream by a ranking
score computed for each of the advertisement items and each of the
content items. The server system transmits a web page including the
stream to a user device over a network. In this manner, advertising
items and content items compete in a unified marketplace for
inclusion in the stream for viewing by the end user.
Inventors: |
Ruiz; Bruno Fernandez
(Sevenoaks, GB), Gaffney; Scott J. (Menlo Park,
CA), Langlois; Jean-Marc (Menlo Park, CA), Krishnamurthy;
Prabhakar (Pleasanton, CA), Lavi; Aharon (Mobile Post
Misgav, IL) |
Applicant: |
Name |
City |
State |
Country |
Type |
Yahoo! Inc. |
Sunnyvale |
CA |
US |
|
|
Assignee: |
Yahoo! Inc. (Sunnyvale,
CA)
|
Family
ID: |
49679326 |
Appl.
No.: |
13/932,766 |
Filed: |
July 1, 2013 |
Current U.S.
Class: |
705/14.41 |
Current CPC
Class: |
G06Q
30/0247 (20130101); G06Q 30/0277 (20130101); G06Q
30/0241 (20130101) |
Current International
Class: |
G06Q
30/00 (20120101) |
Field of
Search: |
;705/14.41 |
References Cited
[Referenced By]
U.S. Patent Documents
Other References
Extended European Search Report issued in European Patent
Application No. 13 193 381.4, dated Jun. 2, 2014, 5 pages. cited by
applicant.
|
Primary Examiner: Li; Sun
Attorney, Agent or Firm: Brinks Gilson & Lione
Claims
The invention claimed is:
1. A system comprising: a processor and a memory; an advertisement
storage device configured to store data defining a plurality of
advertisement items originating with advertisers; a content storage
device configured to store a plurality of content items originating
with sources other than advertisers; and a server system in data
communication with the ad storage device and the content storage
device and configured to display advertisement items retrieved from
the ad storage device and content items retrieved from the content
storage device as a stream of graphical or textual items arranged
in a sequence on a web page and viewable by a user on a user
device, the server system further configured to determine
respective bid amounts for respective advertisements items and to
determine a respective content bid amount for respective content
items that allows ranking together the respective advertisement
items and the respective content items, the server system further
configured to compute respective ranking scores for the respective
advertisement items using the determined respective bid amounts and
to compute respective ranking scores for the respective content
items using the determined respective content bid amounts, the
server system further configured to order the ranked advertisement
items and the ranked content items together in the stream using the
respective ranking scores computed for each respective
advertisement item of the plurality of advertisement items and each
respective content item of the plurality of content items, the
server system further configured to transmit a web page including
the stream to a user device over a network.
2. The system of claim 1 wherein the server system is further
configured to compute a respective ranking score for each
respective advertisement item of the plurality of advertisement
items as a product of at least a bid amount and a clickability
score reflecting a likelihood of being clicked by a user of the
user device independent of the position occupied within the
stream.
3. The system of claim 2 wherein the server system is further
configured to compute the respective ranking score as a product of
the clickability score using a reference click-through-rate
value.
4. The system of claim 2 wherein the server system is further
configured to compute the respective ranking score as a product of
the bid amount, the clickability score and a satisfaction score for
the each respective advertisement item.
5. The system of claim 4 wherein the server system is further
configured to determine the respective content bid amount for each
respective content item by determining a quality score for the each
respective content item and using the quality score to determine
the respective content bid amount for the each respective content
item.
6. The system of claim 5 wherein the server system is further
configured to use the quality score to determine a percentile score
and to use the percentile score to select a bid amount for an
advertisement item having the same percentile score as the
respective content bid amount for the each respective content
item.
7. The system of claim 2 wherein the server system is configured to
detect selection of an ordered advertisement item at a user device
and, in response, to charge an account of an advertiser associated
with the selected advertisement item an amount equal to a bid
amount of a next-lowest-ordered content item or advertisement item
in the stream.
8. The system of claim 1 further comprising: an account database
configured to store account data defining account information for a
plurality of advertisers associated with the plurality of
advertisement items; and an account server in data communication
with the account database and the server system, the account server
configured to respond to a detected selection at a user device of
an ordered advertisement item in the stream by charging an account
of an advertiser associated with the selected advertisement item an
amount equal to a bid amount of a next-lowest-ordered content item
or advertisement item in the stream.
9. The system of claim 1 wherein the server system is further
configured to: compute a clickability score and a post-click
satisfaction score for respective content items and respective
advertising items; compute the respective ranking score for each
advertising item and each content item using the computed
clickability score and the computed post-click satisfaction score
for each of the respective advertising items and the respective
content items and using the determined respective content bid
amount for the respective content items and using an advertiser
specified bid amount for the respective advertising items; using
computed ranking scores for each respective advertisement item and
each respective content item, position the respective advertisement
items having scores greater than processed content items before the
processed content items; and display the respective advertising
items with the respective content items as positioned using the
computed ranking scores.
10. A method comprising: in a server system, retrieving stored
advertisement items from an advertisement storage device, the
advertisement items originating with advertisers; retrieving stored
content items from a content storage device, the content items
originating with sources other than advertisers; determining
respective content bid amounts for respective retrieved content
items that allow ranking together the respective retrieved
advertisement items and the respective retrieved content items;
determining by a processor content ranking scores for the
respective retrieved content items using the respective content bid
amounts; determining by a processor respective advertisement bid
amounts for respective retrieved advertisement items; determining
by a processor advertisement ranking scores for the respective
retrieved advertisement items using the respective advertisement
bid amounts; ranking by a processor the respective retrieved
advertisement items and the respective retrieved content items
using the content ranking scores and the advertisement ranking
scores; formatting a web page with a sequence of items viewable on
the web page when the web page is displayed on a user device, the
sequence of items including the ranked advertisement items and the
ranked content items positioned respectively in the sequence of
items on the web page according to the ranking; and transmitting
the formatted web page to a user.
11. The method of claim 10 wherein ranking the respective retrieved
advertisement items and the respective retrieved content items
comprises scaling the respective retrieved advertisement items to
determine a respective advertisement score for each respective
retrieved advertisement item; scaling the respective retrieved
content items to determine a respective content score for each
respective retrieved content item; and ordering the respective
retrieved advertisement items and respective retrieved content
items on the web page using the respective advertisement scores and
the respective content scores.
12. The method of claim 10 wherein formatting the web page
comprises: arranging on the web page a stream of advertisement
items and content items, the respective retrieved advertisement
items and the respective retrieved content items being ordered
according to the ranking.
13. The method of claim 10 further comprising: for each respective
retrieved advertisement item, determining a respective
advertisement score using a respective advertisement clickability
value; for each respective retrieved content item, determining a
respective content ranking score using a respective content
clickability value; and wherein ranking the respective retrieved
advertisement items and respective retrieved content items
comprises using the determined respective content scores and the
determined respective content scores.
14. The method of claim 13 further comprising: for each respective
retrieved advertisement item, determining the respective
advertisement clickability value as a ratio of how many more clicks
the respective retrieved advertisement item would get to an average
number of clicks received by an inventory of advertisement items;
and for each respective retrieved content item, determining the
respective content clickability value as a ratio of how many more
clicks the respective retrieved content item would receive to an
average number of clicks received by an inventory of content
items.
15. The method of claim 14 further comprising: determining a dwell
time for the respective retrieved content item; and determining the
post-click satisfaction score using the dwell time.
16. The method of claim 13 wherein determining a respective content
score comprises determining the respective content score as a
product of a clickability score and a post-click satisfaction
score.
17. The method of claim 10 further comprising: for each respective
retrieved advertisement item, determining a respective
advertisement bid amount, an advertisement clickability score and
an advertisement satisfaction score; determining a respective
advertisement ranking score as a product of the respective
advertisement bid amount, the respective advertisement clickability
score and the respective advertisement satisfaction score; for each
respective retrieved content item, determining a respective content
clickability score and a respective content satisfaction score;
determining a respective content ranking score as a product of the
respective content bid amount, the respective content clickability
score and the content satisfaction score; and ranking the
respective retrieved advertisement items and respective retrieved
content items by comparing the determined respective content scores
and the determined respective content scores to order the
respective retrieved advertisement items and respective retrieved
content items on the web page.
18. The method of claim 17 further comprising: using a bid amount
associated with each respective advertisement, determining a
respective advertisement score for each respective retrieved
advertisement item; ordering the respective retrieved advertisement
items and respective retrieved content items in a stream on the web
page using the respective advertisement scores and the respective
content scores; detecting a click through of a respective
advertisement by a user viewing the web page; and charging an
account of an advertiser associated with the clicked respective
advertisement an amount equal to a bid amount of a
next-lowest-ordered content item or advertisement item.
Description
BACKGROUND
This application relates generally to data processing systems. More
particularly, this application relates to systems and methods for
displaying revenue-generating information such as advertisements
and non-revenue-generating information such as content together
online.
Online advertising has become increasingly popular as a way for
advertisers to publicize information about goods and services to
potential customers and clients. An advertiser can implement an
advertising campaign using internet-accessible facilities of online
providers such as Yahoo! Inc. The online provider serves to connect
the advertiser with users accessing online resources such as search
engines and news and information sites. Advertisements ("ads") of
the advertiser are provided to the users to inform and attract the
attention of the users.
Some online providers provide a stream of content and other
information on a web page. The web page may be accessed by users on
devices such as desktop computers, portable computers such as
laptops and handheld devices such as tablets and smartphones, or
media devices such as televisions. The stream is presented on the
web page as a sequence of items displayed, one item after another,
for example, down the web page when viewed on the display of a
device. In some cases, the stream may be updated with new content
at the top or bottom of the page upon certain events, such as the
elapse of a certain period of time, the scrolling of a mouse, or
the click of a spacebar.
Advertising items, also referred to herein as "stream ads," are
inserted into the stream of content, supplementing the sequence of
items. Stream ads may be formatted to visually match the
surrounding stream of content so as to appear native to the stream.
Alternatively, stream ads may be formatted to complement the
surrounding stream of content so as to be more eye-catching.
Streams are becoming common in online presentation in part because
they provide added flexibility for web site designers and
advertisers. If a stream is not used to present data on a web page,
the web page must have pre-defined sections. Only certain types of
information, having specified sizes, shapes or content, can be
presented in the pre-defined sections. A stream allows any number
and size and shape of content to be included. A stream also lowers
the cognitive load on the viewer when processing information
associated with different items of content or advertisements by
removing the cognitive overhead associated with switching to a
different visual format or perspective.
It is desirable to manage the flow of content and advertisements in
a stream in order to, in turn, manage the experience of users and
advertisers who interact with the online provider. Further
limitations and disadvantages of conventional and traditional
approaches will become apparent to one of skill in the art, through
comparison of such systems with some aspects of the disclosure set
forth in the remainder of the present application with reference to
the drawings.
BRIEF SUMMARY
In accordance with the systems, products and methods disclosed
here, an online provider may control the location, number and
spatial and temporal frequency of stream ads in a stream of content
viewable by a user on a web page. The stream may be viewed as a
unified marketplace in which both revenue-generating items and
non-revenue-generating items such as advertising items and content
items, respectively, compete for presentation in the stream.
Scoring, ranking, and pricing techniques permit commensurate
treatment of all items (whether revenue generating for the online
provider or not). Additional business rules for content items and
advertising items provide further degrees of freedom for the online
provider in determining how content items and advertisement items
are presented in a stream.
For the online provider, controlling the location, number and
frequency of ads in a stream can help manage the experience of
users and of advertisers with the web site of the online provider.
Providing too many ads can result in a less satisfying experience
for a user. Providing too few ads can reduce or eliminate
advertiser involvement with the web site. Selecting the most
appropriate content items and advertisement items for the user can
keep the user engaged with the web site and ensure the user will
return to the web site. User engagement in turn drives the
confidence and involvement advertisers who place stream ads on the
web site. The present disclosure generally describes a unified
marketplace in which every item of information presented by an
online provider, from revenue generating advertisements to paid
content, is scored, priced according to explicit or implicit bids,
and ranked for presentation in a unitary format, such as a
stream.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 is a block diagram of an example online information
system;
FIG. 2 is an example illustrating a display ad as modified for
display in a stream display;
FIG. 3 is a flow diagram illustrating one embodiment a method for
ranking and displaying a stream of advertising items and content
items in an online information system; and
FIG. 4 is an example process for displaying content in a streaming
media feed according to a quality score computed using clickability
and post-click satisfaction scores.
DETAILED DESCRIPTION
Subject matter will now be described more fully hereinafter with
reference to the accompanying drawings, which form a part hereof,
and which show, by way of illustration, specific exemplary
embodiments. Subject matter may, however, be embodied in a variety
of different forms and, therefore, covered or claimed subject
matter is intended to be construed as not being limited to any
example embodiments set forth herein; example embodiments are
provided merely to be illustrative. Likewise, a reasonably broad
scope for claimed or covered subject matter is intended. Among
other things, for example, subject matter may be embodied as
methods, devices, components, or systems. The following detailed
description is, therefore, not intended to be limiting on the scope
of what is claimed.
Throughout the specification and claims, terms may have nuanced
meanings suggested or implied in context beyond an explicitly
stated meaning. Likewise, the phrase "in one embodiment" as used
herein does not necessarily refer to the same embodiment and the
phrase "in another embodiment" as used herein does not necessarily
refer to a different embodiment. It is intended, for example, that
claimed subject matter includes combinations of example embodiments
in whole or in part.
In general, terminology may be understood at least in part from
usage in context. For example, terms, such as "and", "or", or
"and/or," as used herein may include a variety of meanings that may
depend at least in part upon the context in which such terms are
used. Typically, "or" if used to associate a list, such as A, B or
C, is intended to mean A, B, and C, here used in the inclusive
sense, as well as A, B or C, here used in the exclusive sense. In
addition, the term "one or more" as used herein, depending at least
in part upon context, may be used to describe any feature,
structure, or characteristic in a singular sense or may be used to
describe combinations of features, structures or characteristics in
a plural sense. Similarly, terms, such as "a," "an," or "the,"
again, may be understood to convey a singular usage or to convey a
plural usage, depending at least in part upon context. In addition,
the term "based on" may be understood as not necessarily intended
to convey an exclusive set of factors and may, instead, allow for
existence of additional factors not necessarily expressly
described, again, depending at least in part on context.
An online information system places advertisements of advertisers
within content services made available to end users, such as web
pages, mobile applications ("apps"), TV apps, or other audio or
visual content services. The advertisements are provided along with
other content. The other content may include any combination of
text, graphics, audio, video, or links to such content. The
advertisements are conventionally selected based on a variety of
criteria including those specified by the advertiser. The
advertiser conventionally defines an advertising campaign to
control how and when advertisements are made available to users and
to specify the content of those advertisements.
Streams are becoming common in online presentation because they
provide flexibility for content providers who source content items
for the stream, advertisers who source advertising items for the
stream and for the online provider who combines the content items
and the advertising items to produce the stream. A stream allows
any number and size and shape of content items and advertising
items to be included in the stream. The elements of the stream may
be sorted by relevance or by any suitable parameter. A stream also
lowers the cognitive load on the viewer when processing when
processing information associated with different items of content
or advertisements by removing the cognitive overhead associated
with switching to a different visual format or perspective.
The stream may be viewed as a unified marketplace where content
items and advertising items compete for placement or inclusion in
the stream. The participants in the marketplace are advertisers who
sponsor or provide the advertising items and content providers who
sponsor or provide the content items. The stream and the
marketplace may be hosted or managed by an online provider such as
Yahoo! Inc. The online provider may also provide ads for its own
products and services or its own content items to the stream.
Advertisers interact with equipment of the online provider to
create or provide online advertisements. The online advertisements
include advertising content stored in a database or other memory in
association with identification of the advertiser and one or more
bid amounts. The advertising content may include text or graphics
or both and a link to a landing page to which the user's browser is
redirected upon clicking the link. The bid amount represents an
amount of money the advertiser will pay upon an event pertaining to
the advertisement. The event may be an impression or viewing of the
advertisement by a user, a click through or other selection of the
advertisement by the user viewing the advertisement, or an action
following viewing the advertisement such as providing credit card
information or an email address. The bid amount may be used for
determining position of the advertisement in the stream in a manner
to be described below. The online advertisement may include other
data as well including data defining how the advertisement will
appear in the stream.
The content items include information on a topic that may be of
interest to a user. This information may include a link to another
web page providing more information about the topic and a summary
of information about the topic. In some embodiments, a content
provider will associate a bid amount with a content item. Similar
to bid amounts for advertisements, the bid amount for a content
item may be based on an impression, a click through, or another
action. Also, the bid amount may be used for determining position
of the content item in the stream in a manner to be described
below. Alternatively, a software based bidding agent may be
employed to automatically bid on behalf of content items.
The content items and advertisement items are in competition for
inclusion in the stream. The competition for slots in the stream
may be cleared using a Generalized Second Price (GSP) auction
mechanism. In a GSP auction, the highest bidder gets the first
slot, the second highest bidder gets the second slot and so.
However, the highest bidder then pays the price bid by the second
highest bidder. This is similar to a sponsored search marketplace
although the bids in sponsored search are expressed differently and
the competition in a sponsored search marketplace is only between
advertisements.
In one embodiment, an advertiser provides targeting predicates, an
ad snippet and a bid. In some embodiments, the advertiser can
provide a budget across multiple triples, referred to as targeting
triples. Targeting predicates may be based on any type of market
segment of interest to advertisers, including in one example,
demographic markets, market segments based gender or age,
behavioral segments based on user profile information, or
geographic markets. The bids may be cost per click (CPC) bids, cost
per impression (CPM) bids or cost per action (CPA) bids. The online
provider may choose not to support all bid types in all
marketplaces.
What advertisers are allowed to bid for in large part determines
their bidding behavior. For the online provider who manages the
unified marketplace, there is a trade-off between allowing
advertisers to bid for very specific targets versus allowing
advertisers to bid for more broad targets.
The online provider may prefer markets that are thick with many
competing advertisers to thin markets with few advertisers. The
thicker the market, the greater the potential for increased revenue
to the online provider. However, many advertisers are very
interested in specific types of user. These narrow-focused users
will likely stay out of the marketplace unless they are allowed to
bid more narrowly. Broad targets lower the average value an
advertiser derives since their ads may be shown to users who may
not be interested in their products. Lower expected values lead to
lower bids.
Some of these trade-offs can be mitigated by pricing for
performance, by using excellent scoring algorithms and by
preventing ads of low relevance from showing in the stream. Pricing
for performance implies charging only when a user responds to an
advertisement. Advertisers would prefer to pay only when users
convert, such as by paying for a product or service. However,
defining and tracking conversions and estimating conversions rates
may be difficult to do reliably, so marketplace operators prefer
charging by clicks which are more easily tracked and estimated.
Charging per click can pose challenges. For example, not all clicks
from users convert into sales for an advertiser. With too many
clicks that do not result in a conversion, a low quality score for
the ad may result.
Broad targeting requires precise scoring methods to maintain good
user and advertiser experiences. Scoring is the process of
assigning a value to an advertisement or content item which value
can then be used for determining which item should be included in
the stream. This precise scoring may require that the online
provider examine not just the ad snippet but also the contents of
the landing page. In some embodiments, an advertisement may include
additional information such as metadata that is automatically
collected or manually provided by the advertiser and used as
signals to the scoring function.
Broad targeting may also add a difficulty in pricing for CPC
advertisements. In pricing the advertisement, it is important to
distinguish between the quality of the match between a keyword and
a search term and the quality of the advertisement. The online
operator may choose to discount advertisers for poor quality
matches, which are the responsibility of the operator of the online
marketplace which does the matching. The online operator may choose
to charge a premium for poor quality advertisements which are the
responsibility of the advertiser.
An exemplary system will now be described in which aspects of the
unified marketplace for advertisement items and content items may
be illustrated and described. Further details and optional
embodiments will be provided in connection with the drawings.
FIG. 1 is a block diagram of online information system 100. The
online information system 100 in the exemplary embodiment of FIG. 1
includes an account server 102, and account database 104, a search
engine 106, an advertisement (ad) server 108, an ad database 110, a
content database 114, a content server 112 and a ranking engine
116. The online information system 100 may be accessible over a
network 120 by one or more advertiser devices such as advertiser
device 122 and by one or more user devices such as user device 124.
In various examples of such an online information system, users may
search for and obtain content from sources over the network 120 or
from the content database 114. Advertisers may provide
advertisements for placement on web pages and other communications
sent over the network to user devices such as the user device 124.
The online information system in one example is deployed and
operated by an online provider such as Yahoo! Inc.
The account server 102 stores account information for advertisers.
The account server 102 is in data communication with the account
database 104. Account information may include one or more database
records associated with each respective advertiser. Any suitable
information may be stored, maintained, updated and read from the
account database 104 by the account management server 102. Examples
include advertiser identification information, advertiser security
information such as passwords and other security credentials, and
account balance information.
The account server 102 may be implemented using any suitable
device. The account management server 102 may be implemented as a
single server, a plurality of servers, or any other type of
computing device known in the art. Preferably, access to the
account server 102 is accomplished through a firewall, not shown,
which protects the account management programs and the account
information from external tampering. Additional security may be
provided via enhancements to the standard communications protocols
such as Secure HTTP or the Secure Sockets Layer.
The account server 102 may provide an advertiser front end to
simplify the process of accessing the account information of an
advertiser. The advertiser front end may be a program, application
or software routine that forms a user interface. In one particular
embodiment, the advertiser front end is accessible as a web site
with one or more web pages that an accessing advertiser may view on
an advertiser device such as advertiser device 122. The advertiser
may view and edit account data and advertisement data using the
advertiser front end. After editing the advertising data, the
account data may then be saved to the account database 104.
The search engine 106 may be a computer system, one or more
servers, or any other computing device known in the art.
Alternatively, the search engine 106 may be a computer program,
instructions, or software code stored on a computer-readable
storage medium that runs on a processor of a single server, a
plurality of servers, or any other type of computing device known
in the art. The search engine 106 may be accessed, for example, by
user devices such as the user device 124 operated by a user over
the network 120. The user device 124 communicates a user query to
the search engine 106. The search engine 106 locates matching
information using any suitable protocol or algorithm and returns
information to the user device 124. The search engine 106 may be
designed to help users find information located on the Internet or
an intranet. In a particular example, the search engine 106 may
also provide to the user device 124 over the network 120 a web page
with content including search results, information matching the
context of a user inquiry, links to other network destinations or
information and files of information of interest to a user
operating the user device 124, as well as a stream of content items
and advertisement items selected for display to the user.
The search engine 106 may enable a device, such as the user device
124 or any other client device, to search for files of interest
using a search query. Typically, the search engine 106 may be
accessed by a client device via one or more servers or directly
over the network 120. The search engine 106 may, for example, in
one illustrative embodiment, comprise a crawler component, an
indexer component, an index storage component, a search component,
a ranking component, a cache, a profile storage component, a logon
component, a profile builder, and one or more application program
interfaces (APIs). The search engine 106 may be deployed in a
distributed manner, such as via a set of distributed servers, for
example. Components may be duplicated within a network, such as for
redundancy or better access.
The ad server 108 operates to serve advertisements to user devices
such as the user device 124. Advertisements include data defining
advertisement information that may be of interest to a user of a
user device. An advertisement may include text data, graphic data,
image data, video data, or audio data. An advertisement may further
include data defining one or more links to other network resources
providing such data. The other locations may be other locations on
the internet, other locations on an intranet operated by the
advertiser, or any access.
For online information providers, advertisements may be displayed
on web pages resulting from a user-defined search based at least in
part upon one or more search terms. Advertising may be beneficial
to users, advertisers or web portals if displayed advertisements
are relevant to interests of one or more users. Thus, a variety of
techniques have been developed to infer user interest, user intent
or to subsequently target relevant advertising to users.
One approach to presenting targeted advertisements includes
employing demographic characteristics (e.g., age, income, sex,
occupation, etc.) for predicting user behavior, such as by group.
Advertisements may be presented to users in a targeted audience
based at least in part upon predicted user behavior.
Another approach includes profile-type ad targeting. In this
approach, user profiles specific to a user may be generated to
model user behavior, for example, by tracking a user's path through
a web site or network of sites, and compiling a profile based at
least in part on pages or advertisements ultimately delivered. A
correlation may be identified, such as for user purchases, for
example. An identified correlation may be used to target potential
purchasers by targeting content or advertisements to particular
users.
Yet another approach includes targeting based on content of a web
page requested by a user. Advertisements may be placed on a web
page or in association with other content that is related to the
subject of the advertisements. The relationship between the content
and the advertisement may be determined in any suitable manner. The
overall theme of a particular web page may be ascertained, for
example, by analyzing the content presented therein. Moreover,
techniques have been developed for displaying advertisements geared
to the particular section of the article currently being viewed by
the user. Accordingly, an advertisement may be selected by matching
keywords/and or phrases within the advertisement and the web page.
One exemplary system and method are disclosed in U.S. patent
application Ser. No. 13/836,052, filed Mar. 15, 2013, pending,
entitled Efficient Matching of User Profiles with Audience Segments
for Audience Buy. This application is incorporated herein in its
entirety by this reference.
The ad server 108 includes logic and data operative to format the
advertisement data for communication to the user device. The ad
server 108 is in data communication with the ad database 110. The
ad database 110 stores information including data defining
advertisements to be served to user devices. This advertisement
data may be stored in the ad database 110 by another data
processing device or by an advertiser. The advertising data may
include data defining advertisement creatives and bid amounts for
respective advertisements.
For example, the advertising data may be formatted to an
advertising item which may be included in a stream of content items
and advertising items provided to a user device. The formatted
advertising items are specified by appearance, size, shape, text
formatting, graphics formatting and included information, which may
all be standardized to provide a consistent look for all
advertising items in the stream. At least some advertising items
may have an associated bid amount and may be considered to be
revenue generating items. The ad server 108 then provides the
advertising items to other network devices such as the ranking
engine 116.
Further, the ad server 108 is in data communication with the
network 120. The ad server 108 communicates ad data and other
information to devices over the network 120. This information may
include advertisement data communicated to a user device. This
information may also include advertisement data and other
information communicated with an advertiser device such as the
advertiser device 122. An advertiser operating an advertiser device
may access the ad server 108 over the network to access information
including advertisement data. This access may include developing
advertisement creatives, editing advertisement data, deleting
advertisement data, setting and adjusting bid amounts and other
activities.
The ad server 108 may provide an advertiser front end to simplify
the process of accessing the advertising data of an advertiser. The
advertiser front end may be a program, application or software
routine that forms a user interface. In one particular embodiment,
the advertiser front end is accessible as a web site with one or
more web pages that an accessing advertiser may view on the
advertiser device. The advertiser may view and edit advertising
data using the advertiser front end. After editing the advertising
data, the advertising data may then be saved to the ad database 110
for subsequent communication in advertisements to a user
device.
The advertisement server 108 may be a computer system, one or more
servers, or any other computing device known in the art.
Alternatively, the advertisement server 108 may be a computer
program, instructions and/or software code stored on a
computer-readable storage medium that runs on a processor of a
single server, a plurality of servers, or any other type of
computing device known in the art.
The content server 112 is in data communication with the content
database 114, the ad server 108 and the ranking engine 116. The
content server 112 may access information about content items from
either the content database 114 or from another location accessible
over the network 120. The content server 112 communicates data
defining content items and other information to devices over the
network 120. This information may include content data communicated
to a user device. This information may also include content data
and other information communicated with a content provider
operating a content provider device. A content provider operating a
content provider device may access the content server 112 over the
network 120 to access information including content data. This
access may include developing content items, editing content items,
deleting content items, setting and adjusting bid amounts and other
activities.
The content server 112 may provide a content provider front end to
simplify the process of accessing the content data of a content
provider. The content provider front end may be a program,
application or software routine that forms a user interface. In one
particular embodiment, the content provider front end is accessible
as a web site with one or more web pages that an accessing content
provider may view on the content provider device. The content
provider may view and edit content data using the content provider
front end. After editing the content data, the content data may
then be saved to the content database 114 for subsequent
communication to a user device.
The content server 112 includes logic and data operative to format
content data and other information for communication to the user
device. For example, the content data may be formatted to a content
item which may be included in a stream of content items and
advertisement items provided to a user device. The formatted
content items are specified by appearance, size, shape, text
formatting, graphics formatting and included information, which may
all be standardized to provide a consistent look for all content
items in the stream. In some embodiments, the content items have an
associate bid amount which may be used for ranking or positioning
the content items in a stream of items presented to a user device.
In other embodiments, the content items do not include a bid amount
or the bid amount is not used for ranking the content items. Such
content items may be considered to be non-revenue generating items.
The content server 112 then provides the content items to other
network devices such as the advertising server 108 and the ranking
engine 116.
The ranking engine 116 is in data communication with the ad server
108, the ad database 110, the content server 112 and the content
database 114. The ranking engine 118 is configured to identify
items to be included in a stream of content items and advertising
items to be provided to a user device such as user device 124. The
ranking engine 118 may thus be configured to determine which
advertising items and which content items are qualified to be
included in the stream and to score and to order respective
advertising items and respective content items in the stream.
In one embodiment, the ranking engine 116 is configured to
calculate a ranking score for each of a plurality of advertising
items using bid values retrieved from the ad database 110. The
ranking engine 116 is further configured to calculate a ranking
score for each of a plurality of content items using bid values
obtained from the content database 114. The ranking engine 116 may
use other information available from the ad server 108, the ad
database 110, the content server 112 and the content database 114
as well as the account database 104 when determining the ranking
scores. Other embodiments and other detail of exemplary operation
of the online information system 100 including the ranking engine
will be described below.
The account server 102, the search engine 106, the ad server 108,
the content server 112 and the ranking engine 114 may be
implemented as any suitable computing device. A computing device
may be capable of sending or receiving signals, such as via a wired
or wireless network, or may be capable of processing or storing
signals, such as in memory as physical memory states, and may,
therefore, operate as a server. Thus, devices capable of operating
as a server may include, as examples, dedicated rack-mounted
servers, desktop computers, laptop computers, set top boxes,
integrated devices combining various features, such as two or more
features of the foregoing devices, or the like.
Servers may vary widely in configuration or capabilities, but
generally a server may include one or more central processing units
and memory. A server may also include one or more mass storage
devices, one or more power supplies, one or more wired or wireless
network interfaces, one or more input/output interfaces, or one or
more operating systems, such as Windows Server, Mac OS X, Unix,
Linux, FreeBSD, or the like.
The account server 102, the search engine 106, the ad server 108,
the content server 112 and the ranking engine 114 may be
implemented as online server systems or may be in communication
with online server systems. An online server system may include a
device that includes a configuration to provide content via a
network to another device including in response to received
requests for page views. An online server system may, for example,
host a site, such as a social networking site, examples of which
may include, without limitation, Flicker, Twitter, Facebook,
LinkedIn, or a personal user site (such as a blog, vlog, online
dating site, etc.). An online server system may also host a variety
of other sites, including, but not limited to business sites,
educational sites, dictionary sites, encyclopedia sites, wikis,
financial sites, government sites, etc.
An online server system may further provide a variety of services
that include, but are not limited to, web services, third-party
services, audio services, video services, email services, instant
messaging (IM) services, SMS services, MMS services, FTP services,
voice over IP (VoIP) services, calendaring services, photo
services, or the like. Examples of content may include text,
images, audio, video, or the like, which may be processed in the
form of physical signals, such as electrical signals, for example,
or may be stored in memory, as physical states, for example.
Examples of devices that may operate as an online server system
include desktop computers, multiprocessor systems,
microprocessor-type or programmable consumer electronics, etc. The
online server system may not be under common ownership or control
with the ad server 108, the content server 112 or the ranking
engine 116.
The network 120 may include any data communication network or
combination of networks. A network may couple devices so that
communications may be exchanged, such as between a server and a
client device or other types of devices, including between wireless
devices coupled via a wireless network, for example. A network may
also include mass storage, such as network attached storage (NAS),
a storage area network (SAN), or other forms of computer or machine
readable media, for example. A network may include the Internet,
one or more local area networks (LANs), one or more wide area
networks (WANs), wire-line type connections, wireless type
connections, or any combination thereof. Likewise, sub-networks,
such as may employ differing architectures or may be compliant or
compatible with differing protocols, may interoperate within a
larger network such as the network 120. Various types of devices
may, for example, be made available to provide an interoperable
capability for differing architectures or protocols. As one
illustrative example, a router may provide a link between otherwise
separate and independent LANs. A communication link or channel may
include, for example, analog telephone lines, such as a twisted
wire pair, a coaxial cable, full or fractional digital lines
including T1, T2, T3, or T4 type lines, Integrated Services Digital
Networks (ISDNs), Digital Subscriber Lines (DSLs), wireless links
including satellite links, or other communication links or
channels, such as may be known to those skilled in the art.
Furthermore, a computing device or other related electronic devices
may be remotely coupled to a network, such as via a telephone line
or link, for example.
The advertiser device 122 includes any data processing device which
may access the online information system 100 over the network 120.
The advertiser device 122 is operative to interact over the network
120 with the account server 102, the search engine 106, the ad
server 108, the ranking engine 116, content servers and other data
processing systems. The advertiser device 122 may, for example,
implement a web browser for viewing web pages and submitting user
requests. The advertiser device 122 may communicate data to the
online information system 100, including data defining web pages
and other information. The advertiser device 122 may receive
communications from the online information system 100, including
data defining web pages and advertising creatives.
In some embodiments, content providers may access the online
information system 100 with content provider devices which are
generally analogous to the advertiser devices in structure and
function. The content provider devices provide access to content
data in the content database 114, for example.
The user device 124 includes any data processing device which may
access the online information system 100 over the network 120. The
user device 124 is operative to interact over the network 120 with
the search engine 106. The user device 124 may, for example,
implement a web browser for viewing web pages and submitting user
requests. A user operating the user device 124 may enter a search
request and communicate the search request to the online
information system 100. The search request is processed by the
search engine and search results are returned to the user device
124. In other examples, a user of the user device 124 may request
data such as a page of information from the online information
processing system 100. The data instead may be provided in another
environment such as a native mobile application, TV application, or
an audio application. The online information processing system 100
may provide the data or re-direct the browser to another web site.
In addition, the ad server may select advertisements from the ad
database 110 and include data defining the advertisements in the
provided data to the user device 124.
The advertiser device 122 and the user device 124 operate as a
client device when accessing information on the online information
system 100. A client device such as the advertiser device 122 and
the user device 124 may include a computing device capable of
sending or receiving signals, such as via a wired or a wireless
network. A client device may, for example, include a desktop
computer or a portable device, such as a cellular telephone, a
smart phone, a display pager, a radio frequency (RF) device, an
infrared (IR) device, a Personal Digital Assistant (PDA), a
handheld computer, a tablet computer, a laptop computer, a set top
box, a wearable computer, an integrated device combining various
features, such as features of the forgoing devices, or the like. In
the example of FIG. 1, both laptop computer 126 and smartphone 128
may be operated as either an advertiser device or a user
device.
A client device may vary in terms of capabilities or features.
Claimed subject matter is intended to cover a wide range of
potential variations. For example, a cell phone may include a
numeric keypad or a display of limited functionality, such as a
monochrome liquid crystal display (LCD) for displaying text. In
contrast, however, as another example, a web-enabled client device
may include one or more physical or virtual keyboards, mass
storage, one or more accelerometers, one or more gyroscopes, global
positioning system (GPS) or other location-identifying type
capability, or a display with a high degree of functionality, such
as a touch-sensitive color 2D or 3D display, for example. A client
device such as the advertiser device 122 and the user device 124
may include or may execute a variety of operating systems,
including a personal computer operating system, such as a Windows,
iOS or Linux, or a mobile operating system, such as iOS, Android,
or Windows Mobile, or the like. A client device may include or may
execute a variety of possible applications, such as a client
software application enabling communication with other devices,
such as communicating one or more messages, such as via email,
short message service (SMS), or multimedia message service (MMS),
including via a network, such as a social network, including, for
example, Facebook, LinkedIn, Twitter, Flickr, or Google+, to
provide only a few possible examples. A client device may also
include or execute an application to communicate content, such as,
for example, textual content, multimedia content, or the like. A
client device may also include or execute an application to perform
a variety of possible tasks, such as browsing, searching, playing
various forms of content, including locally stored or streamed
video, or games. The foregoing is provided to illustrate that
claimed subject matter is intended to include a wide range of
possible features or capabilities.
FIG. 2 illustrates streams of content items and data items
displayed on selected user devices. In FIG. 2, a display ad 202 is
illustrated as displayed on a variety of displays including a
mobile web device display 204, a mobile application display 206 and
a personal computer display 208. The mobile web device display 204
may be shown on the display screen of a mobile handheld device such
as a smartphone. The mobile application display 206 may be shown on
the display screen of a portable device such as a tablet computer.
The personal computer display 208 may be displayed on the display
screen of a personal computer (PC).
The display ad 202 is shown in FIG. 2 formatted for display on a
user device but not as part of a stream to illustrate an example of
the contents of such a display ad. The display ad 202 includes text
212, graphic images 214 and a defined boundary 216. The display ad
202 is developed by an advertiser for placement on a web page sent
to a user device operated by a user. The display ad 202 may be
placed in a wide variety of locations on the web page. However, the
defined boundary 216 and the shape of the display ad must be
matched to a space available on a web page. If the space available
has the wrong shape or size, the display ad 202 may not be
useable.
To overcome these requirements and limitations, the display ad 202
may be reformatted or alternately formatted for inclusion in a
stream of content items and advertising items including a stream ad
incorporating contents of the display ad 202.
In these examples, the display ad is shown as a part of streams
224a, 224b, and 224c. The streams 224a, 224b, 224c include a
sequence of items displayed, one item after another, for example,
down a web page viewed on the mobile web device display 204, the
mobile application display 206 and the personal computer display
208. The streams 224a, 224b, 224c may include any type of items. In
the illustrated example, the streams 224a, 224b, 224c includes
content items and advertising items. For example, stream 224a
includes content items 226a and 228a along with advertising item
222a; stream 224b includes content items 226b, 228b, 230b, 232b,
234b and advertising item 222b; and stream 224c includes content
items 226c, 228c, 230c, 232c and 234c and advertising item 222c.
Each of the streams 224a, 224b, 224c may include any number of
content items and advertising items. In one embodiment, the streams
224a, 224b, 224c may be arranged to appear to the user to be an
endless sequence of items so that as a user of a user device on
which one of the streams 224a, 224b, 224c is displayed scrolls the
display, a seemingly endless sequence of items appears in the
displayed stream.
The content items positioned in any of streams 224a, 224b, 224c may
include news items, business-related items, sports-related items,
etc. Further, in addition to textual or graphical content, the
content items of any stream may include other data as well, such as
audio and video data or applications. Each content item may include
text, graphics, other data, and a link to additional information.
Clicking or otherwise selecting the link re-directs the browser on
the user's device to a web page referred to as a landing page that
contains the additional information.
Stream ads like the advertising items 222a, 222b, and 222c may be
inserted into the stream of content, supplementing the sequence of
related items, providing a more seamless experience for end users.
Similar to content items, the advertising items may include textual
or graphical content as well as other data such as audio and video
data or applications. Each advertising item 222a, 222b, and 222c
may include text, graphics, other data, and a link to additional
information. Clicking or otherwise selecting the link re-directs
the browser on the user's device to a web page referred to as a
landing page.
While the exemplary streams 224a, 224b, 224c are shown with a
single visible advertising item 222a, 222b, 222c, respectively, any
number of advertising items may be included in a stream of items.
Conventionally, it has been known to position the advertising items
at fixed locations. For example, in one conventional system, it was
known to position an advertising item at the third item in the
stream, counting from the top, at the sixteenth item in the stream
and at every thirteenth item in the stream thereafter. That is, in
the conventional system, advertisements are located in pre-defined
slots in the stream. Slotting of the advertisements is the same for
all users under all conditions. In this regard, advertisements and
content items are complements in the stream. If a content item is
not placed at a designated slot in the stream, an advertisement is
placed in that slot.
In accordance with one aspect of the illustrated embodiment,
slotting of advertisements in a stream is made dynamic. Any slot in
the stream is subject to competition between advertising items and
content items. A score is determined for each respective item. The
scores for the advertising items and the content items are made
commensurate so that advertising items and content items may be
ranked against each other and the ranking used to populate the
stream. Techniques for ranking the advertising items and content
items are discussed in further detail below.
FIG. 3 is a flow diagram illustrating one embodiment a method for
ranking and displaying a stream of advertising items and content
items in an online information system. The method of FIG. 3 may be
performed by, for example, elements of the online information
system 100 of FIG. 1 including the account server 102, the search
engine 106, the ad server 108, the content server 112 and the
ranking engine 114. In other embodiments, other components may be
involved in performing the method of FIG. 3 and some of the steps
illustrated for the method of FIG. 3 may be omitted or reordered
and different steps may be added or substituted.
The method begins at block 300. At block 302, the method waits in a
loop for receipt of a page view request. A page view request is a
data communication received over a network such as the network 120
of FIG. 1. The data communication includes data specifying a web
page to view. For example, the page view request may specify the
uniform resource locator (URL) for an online provider such as
Yahoo! including the URL yahoo.com. The requested web page is one
which may be populated in full or in part by a stream including
items of at least two different types. In the examples shown here,
the types of items included in the stream are content items and
advertising items, generally as shown in the example embodiment of
FIG. 2. However, in other embodiments, other types of items may be
provided in a stream, and the types of items or categories of items
may be selected according to any convenient or useful criteria. For
example, instead of populating a stream with content items and
advertising items as shown in FIG. 2, a stream may be populated
with sports-related items of content and news-related items of
content. In another example, instead of just two types of items
being scored and ranked together such as content items and
advertising items, more than two items may be scored and ranked,
including content items, CPC advertising items and CPM advertising
items. The method illustrated in FIG. 3 may be extended to the
widest variety of combinations.
After a page view request has been received, at block 304,
advertisement items and content items are qualified so that only
qualified items are the subject of further processing. Items
selected for qualification are contained, in one example, in the ad
database 110 and the content database 114 of the online information
system 100 of FIG. 1.
Qualification may be performed on any suitable basis using any
suitable inputs. For example, advertising items and content items
may be qualified based on identification information for a user
from whom the page view request is received. If the online
information system already stores information about interests and
preferences of the identified user, that information may be used to
qualify advertising items and content items. Also, if advertisers
have specified targeting constraints, such as gender, age and
geography, those constraints may be applied to information known
about the user to qualify advertising items and content items.
Still further, if the page request includes information specifying
a device type or platform of the user device, such as smartphone or
tablet or desktop computer, that platform information may be used
to qualify advertising items and content items for further
processing. Some content providers may limit the content items they
will send to particular platforms or format the content items to a
particular format based on the platform information. Similarly,
some advertisers may direct particular advertisement items only to
desktop computers or tablet computers. Once the content items and
advertising items have been qualified, processing continues to
block 306.
At block 306 a clickability score is computed for each advertising
item and a clickability score is computed for each content item.
Clickability is a measure of how many more clicks a given
advertising item or content item gets compared to the average
advertising item or content item. In one example, clickability is a
function of number of clicks an advertising item or content item
receives for all users and number of impressions or views an ad
receives for all users, and the click through rate (CTR) for the
advertising item or the content item for all users. Clickability is
position independent.
Click through rate is defined as the ratio of clicks to impressions
an advertising item or content items receives. Click through rate
may be determined dynamically using stored data such as statistical
data about performance of advertisements in the online information
system. For example, each time a particular advertising item or a
particular content item is displayed or viewed by an advertiser, a
data item representing views or impressions for that item is
incremented. Similarly, each time a particular advertising item or
a particular content item is clicked or otherwise selected by a
user, a data item representing clicks or click throughs for that
item is incremented. Data items may be stored in an ad database, a
content database or any other suitable storage device such as the
ad database 110 and the content database 114 of FIG. 1. Similarly,
mathematical processing may be performed, for example, by the ad
server 108, the content server 112 or the ranking engine 116 of
FIG. 1.
In one example, click through rate CTR is defined as
.times..times..gamma..times..times..gamma. ##EQU00001##
where C.sub.i,t represents the number of click throughs an
advertising item or a content item receives at a particular
position i over time t. Time t represents a discrete increment of
time, the width of which may vary. Typically, each increment of t
corresponds to seconds or minutes. The position i refers to the
position in the stream with i=1 representing the first slot, i=2
the second, etc. In one embodiment, V.sub.i,t represents the number
of views or impressions the advertising item or content item
receives at a particular position i over time t. In another
embodiment, V.sub.i,t represents the sum of clicks and skips for a
position i at time t. A skip for position i may be counted whenever
a user clicks on an item in a position below position i, or may be
adjusted by a coefficient to register a fractional count. For
example, if a user clicks on i=4, positions i=1, 2, 3 have skip
counts incremented at the same time that position i=4 has its click
count incremented. The exponential term with coefficient gamma
(.gamma.) specifies a decay rate, which is typically longer for ad
items than for content. The exponentials introduce time-dependence
so that, if an event such as a click or view occurred recently in
the past, it is given more weight than older events.
In an embodiment, the coefficient gamma (.gamma.) may be computed
or adjusted based on periodic comparisons of the click through rate
of a given item j at position i and time t+1 against the reference
click through rate and clickability for the same item j at the same
position i in the immediately preceding time increment--e.g., by
plotting refCTR(i)*clickability(j,t) against CTR(i, j, t+1). The
refCTR and clickability functions are further described below. In
another embodiment, gamma (.gamma.) is computed by summing and then
minimizing the error:
.gamma..times..function..function..function..function.
##EQU00002##
For computing click through rate, the following decomposition is
assumed for both advertising items and content items:
CTR(ad/content,user,position,configuration)=Clickability(ad/content,user)-
*refCTR(position,configuration)
Thus, click through rate CTR is specified for a particular
advertising item or content item, particular user, particular
position in the stream and particular device configuration.
Examples of device configurations include a handheld device, a
tablet and a desktop computer. Other configurations and
technologies may be accommodated as well and may be used to
characterize CTR or other user data. The relationship between
clickability and click through rate is specified by a reference
curve, refCTR, which varies with position of the content item or
the advertising item in the stream and user device configuration.
Clickability thus explicitly represents a position-independent
CTR.
As a corollary, refCTR represents the probability that a user will
click on a particular advertising item or a particular content item
at a particular position i, independent of any effect on click
through rate of the desirability (or undesirability) of particular
advertising or content items. This refCTR may be computed by
running a random bucket showing click through rates for random
advertising items or content items across all positions, with the
same proportion of advertising/content as in the main bucket.
The value of clickability for an advertising item or content item
is desired to be position independent. Clickability eliminates any
effect of position in the stream of a content item or an
advertising item and focuses instead on the quality of the content
item or the advertising item.
Turning back to step 306 of FIG. 3, a clickability score for an
item i in the stream may therefore be computed using the following
general relation:
.function..times..times..times..gamma..times..times..times..gamma..functi-
on. ##EQU00003##
In some embodiments, however, it may be desirable to estimate or
measure the clickability for an item specific to particular users
or a particular market segment, which may be given an arbitrary
index label j. In such embodiments, clickability may be computed
based on a relation specific to user or market segment
.function..times..times..function..times..times..function..function.
##EQU00004##
Thus, in various embodiments, to determine clickability, one may
sum clicks over all positions and divide by the product of views or
impressions at all positions and refCTR.
In some embodiments, personalization is then introduced as an extra
factor, which contributes to the overall click through rate for a
given item, user/segment, position, and device configuration:
CTR(ad/content,user/segment,position,config)=refCTR(position,configuratio-
n)*Clickability(ad/content,user/segment)*Affinity(ad/content,user/segment)
For content items, the Affinity between user and content is
estimated from historical data using the number of clicks observed
for this user on content items with similar content features (with
similarity determined using algorithmic analyses of similarity to
context or known content taxonomies, for example), normalized by
the number of observed clicks from the reference user segment. In
one embodiment, this is done using a Naive Bayes approximation.
Many months of user historical data may be used for reliably
estimating this affinity for content items.
For advertising items, the affinity between user and an
advertisement may be more difficult to estimate from historical
data. The user buy intent with respect to an advertised product or
service is probably not as long lived as the user's general
interest in a content item such as a news story. Therefore, a
shorter historical window may be used for advertising items. The
exponential variation using gamma (.gamma.) is introduced to adjust
the time window for which the quantity is being computed. The
historical data for advertising items also tends to be much more
sparse. In some embodiments, search history profiles, mail, or
other application activities may be used to expand the pool of user
behaviors. Regardless of the source of data, once the data is
collected, an affinity score is computed by rendering both user
data and items in a high-dimensional vector space defined by
recognized features within an existing content network or taxonomy.
Description of such renderings are provided in co-pending U.S.
patent application Ser. No. 13/839,169, entitled "Method and System
for Multi-Phase Ranking for Content Personalization," and Ser. No.
13/837,357, entitled "Method and System for User Profiling Via
Mapping Third Party Interests to a Universal Interest Space," both
of which are incorporated by reference herein in their
entirety.
After clickability is computed for each advertising item and each
content item at block 306, at block 308, a satisfaction score is
computer for each advertising item and each content item.
Satisfaction may also be referred to as post-click satisfaction and
is defined by some measure of user satisfaction after the user has
interacted with the advertising item or the content item.
Clickability generally only measures the propensity of a user to
click on an advertising item or a content item. Satisfaction
assigns a numerical value to the user's likelihood to return to the
online provider or the marketplace based on the user's overall
experience. In one example, satisfaction may be set to a value
between 0 and 1 where 1 indicates complete user satisfaction and
willingness to return and 0 indicates complete user dissatisfaction
and a lost user who is unlikely to ever return.
In some examples, satisfaction can be estimated for an advertising
item using conversion data. However, such data is relatively sparse
and may not be reliably compared across advertising items. In
another example, dwell time may serve as a measure of satisfaction.
Dwell time is an indication of the amount of time a user views a
clicked-on advertisement after clicking on an advertising item
views content after clicking a content item. In some embodiments,
where insufficient data makes estimation unreliability,
satisfaction may be set to a constant value such as 1.0 for content
items, advertising items or both.
In other embodiments, an additional factor for popularity may be
included in the CTR model:
CTR(ad/content,user/segment,position,config)=refCTR(position,configuratio-
n)*Clickability(ad/content,user/segment)*Affinity(ad/content,user/segment)-
*Popularity(ad/content,user/segment) The popularity score reflects
a measure of overall interest in a particular item within a window
of time. The popularity score may be computed, for example, based
on a simple ranking of the highest click through rates for ads or
content within a window of time. The ranking is then normalized and
adjusted by a coefficient reflecting the relative importance of
Popularity to the overall CTR.
At block 310, a bid amount for each content item is computed. For
advertising items, a bid amount is set by the advertiser and is
stored, for example, in the ad database with the rest of the data
defining the ad. For content items and advertising items to be
ranked together for inclusion in the stream, there must be an
analog to the bid amount of an advertising item for a content item.
In some embodiments, content providers may provide a bid amount if
they are participating in the unified marketplace. In other
embodiments, though, no content bids may exist. Until publishers
and other content providers explicitly bid to position their
content items in the stream, an automatic method may generate bids
that allow ranking of content items and advertising items together.
In still other embodiments, content bids may reflect actual amounts
paid to content contributors who are partnered with an online
provider.
One example for determining content bids is shown here. For each
user or user-segment, Table 1 below may be constructed. Here
user-segment can be based on the targeting profile of the user
(i.e. the combination of user segments that the advertiser can bid
for). That is, if data for a given user is not available, data for
the table can be obtained at an aggregate level based on user
segments.
TABLE-US-00001 TABLE 1 Transformed Historical Bid (f[b.sub.a])
(values below for Quality-Score (q.sub.c) Percentile Historical bid
(b.sub.a) m = 0.7) 0.05 (min value 0 $0.10 (min value $0.3 of
q.sub.c allowed) of bid allowed) 0.15 10 $0.25 $0.5 -- 0.87 90
$1.50 $1.76 1.0 (max value 100 $100 (max $100 of q.sub.c) possible
bid)
Table 1 stores percentile values for content-quality score and
historical bids on ads. These percentiles are not
impression-weighted. Thus the percentile distribution of
quality-scores is over the set of unique content items just as the
distribution of bids is over the set of unique ads. The percentile
score may be obtained by selecting all content items in the ad
database which are qualified for the user, the ranking the selected
content items by quality score. The ranked, selected items are then
segregated according to their tenth percentile, twentieth
percentile, etc. Other techniques may be used as well. Thus, the
table translates quality score for content into a bid for content.
In this manner, the quality score and the bid data are independent
for content items and advertising items. Quality score for contents
and bid data for ads is paired or correlated based only on relative
percentile.
In other embodiments of the unified marketplace, ads and content
may be paired, correlated, or synchronized in other ways. For
example, ads and content may be bundled based on similar subject
matter in order to fit to an estimated overall clickability for the
bundle, which is calibrated to preserve the quality of end user
experience.
The Quality Score entered in Table 1 in one embodiment is a
function of clickability and post-click satisfaction. One technique
for calculating Quality Score is described below in conjunction
with FIG. 4. The historical period of the data may be restricted to
`d` days (where `d` is an external parameter). The right-most
column of Table 1 carries a transformation of the historical bid.
An exemplary transformation function for bid amounts that may be
used is the following:
.function..function..function..function..function. ##EQU00005##
where m.epsilon.(0 . . . M) where M is an input parameter. Values
m<1 give advantage to content items and values m>1 give
advantage to advertising items. Typically m would have a value of
approximately 1. Data in Table 1 can be updated every day by the
online provider.
After content-items have been selected for the stream, upon a page
view event, step 310 of the exemplary method of FIG. 3 includes a
lookup for each content-item from the table by using the Quality
Score q.sub.c as a key to the table. If an entry for q.sub.c is
found, look up the corresponding bid b.sub.a from the third column.
If q.sub.c is not found, the find the immediately higher score with
an entry in the table, designated q.sub.c.sup.h. Then find the bid
in the table which corresponds to q.sub.c.sup.h, b.sub.a. The
transformed historical bid can then be computed as
b.sub.c=k*f[b.sub.a], where k is an external parameter.
Thus, the bid for the content item is determined using the bid for
an advertising item. This technique is exemplary only, but it
ensures that bids automatically determined for content items are
commensurate with bids for advertising items and that each content
item will get a unique bid. These benefits are important for
ranking the content items for inclusion in the stream. Moreover,
the bid for a content item is proportional to its quality score.
Thus, only high-quality advertising items, with high quality
scores, will rise to the top of the stream and at the same time,
content items with low quality scores will not displace
high-quality advertising items. Since the content bid is
proportional to the quality score, it reflects both long-term user
value as well as immediate short term revenue. Still further the
content bid is proportional to the monetary value of the user since
it can be safely assumed that advertisers' bids reflect the value
of the user. Finally, the illustrated technique is adaptive. That
is, as bids on advertising items increase, bids on content items
will increase as well. Up to a point, this will induce advertisers
to bid more.
The external parameters k and m may be chosen in any suitable
manner. In one example, the parameters may be chosen so that most
the time, content items get the top slots in the stream,
advertising items are interspersed with content items and not
clustered together. Also, the marketplace should achieve some level
of stability after an initial boot up phase. This implies
advertisers should not have to constantly increase their bids on
their advertising items to hold their slots. One exception to this
rule is when the marketplace is growing and new advertisers are
entering the marketplace.
At block 312, a ranking score is computed for each advertising item
and for each content item. For content items, in one embodiment,
ranking-score.sub.s=b.sub.s*c.sub.s*s.sub.c
where b.sub.c is the computed bid for the content item, c.sub.c is
the clickability for the content item and s.sub.c is the
satisfaction score for the content item. For advertising items, in
one embodiment, ranking-score=b.sub.a*c.sub.a*s.sub.a
where b.sub.a is the advertiser-specified bid for the advertising
item, c.sub.a is the clickability for the advertising item and
s.sub.a is the satisfaction score for the advertising item.
After computing the ranking score at block 312, at block 314, the
advertising items and the content items are sorted using their
computed ranking scores. Because the ranking scores have been
computed using similar values, the ranking scores are commensurate
and may be reliably be interlaced. The result of the sorting is a
blended slate of advertising items and content items.
Following the sorting step, the blended slate may be used to
populate the stream. However, in some embodiments, it may be
preferred to process the blended slate for diversity, block 316.
Diversity is applied to content to prevent too many similar content
items to be located near each other. An example is news articles on
the same topic that may be ranked close to each other. Only content
items are affected by diversity processing and it will cause some
content items to drop in rank.
One example of a diversity algorithm includes, first, ordering the
content items by ranking-score (b.sub.c*c.sub.c*s.sub.c) so that
position 1 corresponds to the highest score. Second, comparing the
content items in position 2-N , where N is in the range 200-300,
against the content item in position 1. Third, if any item in
positions 2-N matches any features of the item in position 1, then
multiply the item's ranking-score by a diversity score, such as a
value in the range [0-1] that depends on feature type. Fourth, the
content items in positions 2-N may be sorted using the new ranking
scores penalized with diversity against the content item in
position 1. Fifth, steps two through four may be repeated for
content items in positions 3-N in comparison with the content item
in position 2. And sixth, step five of repeating for items in
positions 3-N may be repeated, for example, a minimum of 20
times.
In addition to processing content items for diversity, particular
rules may be applied to advertising items following the sorting
step, as well. For example, in one embodiment, rules may be applied
as guard rails, preventing advertising items from being positioned
in slots 1 and 2 of the stream. If an advertising item is in either
of the first two positions following the sorting process, the
advertising item may be automatically moved to a lower position
such as slot 3. In another embodiment, a rule is established to
maintain at least 9 content items between each advertising item in
the stream. If fewer than 9 content items occur between two
advertising items, the lower-ranked advertising item may be
automatically moved to a lower position. Other values may be chosen
for these rules, other rules may be established as well.
At block 318, pricing is calculated for advertising items and
content items. Pricing refers to the amount an account of an
advertiser associated with an advertising item is charged in
response to selection of the advertising item by a user, such as by
a click through. Similarly, pricing for a content item refers to
the amount an account of a publisher or content provider associated
with a content item is charged in response to selection of the
content item by a user, such as by a click through of that content
item.
In one embodiment a rule known as Generalized Second Price (GSP) is
adopted for block 318. Under this rule, if an advertising item was
clicked on or otherwise selected by a user, the advertiser
associated with the advertising item will be charged an amount
which is equal to the minimum bid required to win that position.
Specifically, let i denote the advertising item a under
consideration, and let i+1 be the content item or advertising item
in the position right below advertising item a. Then, the price
that the advertiser associated with advertising item a will be
charged, if clicked, will be the maximum of a specified reserve
price for item i r.sub.i and the quotient of the product of bid,
clickability, and satisfaction for the item at i+1 and the
clickability and satisfaction for the item at i:
.function..times..times..times. ##EQU00006##
The price p.sub.i is always smaller than the original bid b; since
b.sub.i+1*ci.sub.+1*s.sub.i+1<b.sub.i*c.sub.i*s.sub.i. The price
p.sub.i also has the desirable property of no subsequent regret for
the advertiser. That is, if the advertiser would have chosen a bid
smaller than b.sub.i, the advertiser would still win the same slot.
This is true as long as the advertiser's bid is larger than
p.sub.i. If the bid amount b.sub.i is charged to the advertiser,
after the fact the advertiser would regret not bidding p.sub.i plus
one cent. If instead we charge the advertiser p.sub.i to begin
with, the incentive to out-guess the system is reduced and
advertiser satisfaction is increased. The advertiser would still
need to check whether a different slot would be a better fit,
though. Periodic bid adjustments are recommended.
In some embodiments, only cost per click (CPC) bids are accepted
for advertising items and content items. In other embodiments,
however, cost per impression (CPM) bids may be accepted as well.
When CPM bids are accepted, the desired allocation of bids to slots
is required to place ads with higher expected revenue in higher
slots, where a CPC ad a placed in slot k is assumed to have
expected revenue b.sub.a*c.sub.a*refCTR(k). When all bids are CPC
bids, this allocation goal is achieved by simply sorting the
advertising items according to b.sub.a*c.sub.a since refCTR(k) is
advertisement independent and therefore can be omitted. This makes
the computation of the allocation of bids relatively quick and
straightforward.
On the other hand, the revenue of a CPM ad does not depend in any
way on its clickability or on its slot. The advertiser pays its bid
whenever the ad is being displayed. Simply sorting CPC and CPM bids
according to bid value (or bid amount multiplied by clickability)
will no longer satisfy the property of higher revenue bids getting
a higher position in the stream.
However, the following approach may be used to jointly rank the two
types of advertising items in one pass over slots, still satisfying
the above revenue requirement, and without computational slow down.
Thus, there does not have to be any technical limitation in
allowing both CPM and CPC ads being offered.
The proposed algorithm is as follows.
Input:
1. sorted CPC slate (in order of bid x clickability), x1>x2>
. . . >x.sub.n
2. refCTRs (position-dependent, ad independent) a1>a2> . .
.
3. sorted CPM slate (in order of bid) y>Y2> . . . >Ym
For each slot k (starting with the highest slot, 1),
Let X be the highest-ranked CPC advertising item that isn't
assigned yet
Let Y be the highest-ranked CPM advertising item that isn't
assigned yet
If X*a.sub.k>Y, then assign X to slot k, else assign Y to slot
k.
The pricing technique may be varied for inclusion of CPM ads, as
well. For a CPM ad, price is set at (bid*clickability of the CPM
advertising item or content item below OR CPM of ad below). For a
CPC ad, price is set at (bid*clickability of the advertising item
or content item below)/(clickability of the CPC advertising item)
If the advertising item below is CPC, OR price is set to
(bid/refCTR of advertising item below)/(clickability of the CPC
advertising item) if ad below is CPM.
This technique satisfies the above ranking-by-revenue requirement.
The run time and latency are not affected, since sorting time is
the dominant factor here and sorting is being done even for
CPC-only bids.
Illustrated in FIG. 4 is an example process for displaying content
in a streaming media feed according to a quality score. Also
illustrated is an example process for determining the quality
score. In one example, a user of an electronic device, such as a
mobile device, may be viewing content, such as news articles, in a
streaming media feed. The content (e.g., news articles) may be
interlaced with advertisements in the feed as in the exemplary
illustration of FIG. 2. For example, the user may view a news
article, and when or after the article is viewed, the user may
request a next article; however, prior to that next article, an
advertisement may appear in the feed. In general, the order in
which advertisements and articles appear in the feed may be
arbitrary or be determined by a factor, such as a quality
score.
In one example, the ranking engine of 116 of the online information
system 100 implements a quality scoring system (QSS), the operation
of which is shown schematically in FIG. 4. A processor of the
ranking engine 115 cooperates with data stored in, for example, the
ad database 110 and the content databae 114, to perform the
following data processing operations of the QSS.
The QSS receives information, designated in FIG. 4 as present
content 402, such as an advertisement or an article, to be scored
by the QSS. The present content 402 may be received from a content
source via a network, such as the Internet. The QSS may include an
interface, such as an optical or electrical transceiver, configured
to receive data defining the present content 402 from a streaming
media feed or any other type of online feed. The content source may
be any source of advertisements or multi-media content, such as a
network of servers hosting content configured to feed online
content.
Upon receiving the present content in the streaming media feed, for
example via the interface of the QSS, a processor of the QSS
communicatively coupled to the interface may determine or be
configured to determine a first probability at 404, which is a
probability a user will select to interact with the item of present
content in the streaming media feed. In one example, the item of
present content may be an advertising item or a content item as
described and illustrated herein, for example in conjunction with
FIG. 2.
Regarding the determination of the first probability at 404,
selecting to interact with the present advertisement or article may
include clicking an advertisement or article or clicking a
hyperlink to an advertisement or article. Furthermore, such
selecting may include a gesture made with respect to an
advertisement or article or a gesture made with respect to a
hyperlink to an advertisement or article.
As illustrated, the first probability may be based on data 406,
which corresponds to features of the item of present content
correlating to a user's prior interactions and/or predilections
with content similar to the present content. The interactions
and/or predilections may come from a user profile stored in a
database, such as database 408. The user profile, for example, may
include parameters associated with the user with respect to viewing
of streaming media content, for example, and the data 406 may be
received by the processor from a database 408 of the QSS.
Upon receiving the present content, the processor may determine or
be configured to determine a second probability 410, which is a
probability the user will select to interact with content, such as
an advertisement or an article, in general, in the streaming media
feed. Data 412, which is used as input to determine the second
probability 412, may be received by the processor from the database
408 of the QSS.
The processor may also determine or be configured to determine an
affinity score of the present content. The affinity score is a
relationship between the first determined probability and the
second determined probability at 414. The relationship may be
represented by a ratio or another type of numerical score, for
example. Also, the affinity score may represent a correlation
between a user and a present content item based on features of the
present content item matching user profile parameters associated
with the user.
The affinity score may be independent of a configuration of the
present content item in a streaming media feed. For example, the
affinity score may be independent of a position of the present
content item in a streaming media feed. In other words, the
affinity score may not take into account the order in which the
present content appears with respect to other content in the
streaming media feed.
In one example of the feed, the present content item may be a first
content item, such as an advertisement, and a second content item
may be an article, such as a news article. In such a case, the
first and the second content items may include similar subject
matter categorically and the determining of the affinity score may
include determining an affinity score of the second content item
based on the affinity score of the first content item. In such an
example, the similarity of the subject matter categorically may be
identified by matching metadata elements embedded in the first and
the second content items. The matching metadata elements may
include matching categories of a content taxonomy and/or
associations with a webpage of a series of webpages provided by a
web content provider. In one example, the webpage of a series of
webpages provided by a web content provider may be an online
encyclopedia or dictionary entry, such as a WIKIPEDIA entry.
In another example of the feed, the determination of the affinity
score may include determination of an affinity score between a
device, such as a mobile device, of the user and the present
content. This affinity score between the user's device and the
present content may be based on text in the present content and a
present geographic location of the mobile device, for example. This
affinity score may also be based on any other attribute of the
mobile device, such as a telecommunications service carrier
associated with the device, and on any other attribute of the
present content, such as a video element of the present
content.
In this other example, the processor may determine or be configured
to determine the first probability according to a probability a
user of the device will select to view the present content item,
based on features of the present content item matching profile
parameters associated with the user's device and/or the user.
Regarding the second probability, the processor may determine it
according to a probability a user using the device will select to
view a content item, in general, in the streaming media feed.
Finally, the processor may determine or be configured to determine
the affinity score based on a relationship between the first
determined probability and the second determined probability.
In yet another example, the processor may determine or be
configured to determine the first or the second probability using a
machine learning technique. For example, the processor may
determine the first or the second probability using a boosted
decision tree or another form of artificial intelligence.
The processor may also identify or be configured to identify data
416 corresponding to post-interaction satisfaction with prior
content, such as data regarding post-interaction satisfaction with
prior viewed advertisements or articles that match categorically
the present content. The data 416 may then be derived into a
post-interaction satisfaction score. The categorical matching may
include matching by categories of a content taxonomy and/or
associations with a webpage of a series of webpages provided by a
web content provider such as a provider of an online encyclopedia
or dictionary. The data 416 may include data associated with posts
regarding the present content or similar content, including social
media posts. The data 416 may also include data regarding mouse
clicks on the content or similar content or on links to such
content. Also, the data 416 may include data regarding views of
such content, lengths of viewing such content, registration or
subscriptions to view such content, amount of sharing of such
content to other users, and linking to such content via the user's
own content.
Based on the affinity score and the post-interaction satisfaction
data 416 or the post-interaction satisfaction score, the processor
may determine or be configured to determine the quality score at
418. For example, the quality score may be determined by computing
a product of the affinity score and the post-interaction
satisfaction score.
Upon determining the quality score, the score may be used by the
processor as a basis for displaying the present content and/or
configuring the present content with respect to the feed, such as
the order the present content is displayed relevant to other
content in the feed. Also, the quality score may be displayed or
used in generating reports for administrators, for example.
From the foregoing, it can be seen that the present disclosure
provides techniques for an online provider to control the location,
number and density of steam ads in a stream of content viewable by
a user on a web page. The stream may be viewed as a unified
marketplace where both content items and advertising items compete
for inclusion in the stream. Scoring and ranking techniques permit
commensurate ranking of both content items and advertisement items.
Additional business rules for content items and advertising items
may further control the relative location of content items and
advertisement items in a stream.
The disclosed method and system may be implemented partly in a
server, a client device, a cloud computing environment, partially
in a server and partially in a client device, or a combination of
the server, the cloud computing environment and the client
device.
It is therefore intended that the foregoing detailed description be
regarded as illustrative rather than limiting, and that it be
understood that it is the following claims, including all
equivalents, that are intended to define the spirit and scope of
this disclosure.
* * * * *